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diff --git a/Flights/raw_data/ml_Flights.npy b/Flights/raw_data/ml_Flights.npy new file mode 100644 index 0000000000000000000000000000000000000000..2b2cf7c342a683f058d00a89cc98c19477e6c00f --- /dev/null +++ b/Flights/raw_data/ml_Flights.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a1c9eb09adb5f96f34389bd81c8bdb450d3c3df6f7c8bbe65ca3feeab22da268 +size 15417296 diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..35d790bbdd49563e06ecef25de154c9eaf079167 --- /dev/null +++ b/README.md @@ -0,0 +1,54 @@ +# DATA Inventory + +Scanned from the current checkout on 2026-03-26 UTC. This document focuses on the DGB datasets currently kept under `DATA/`. + +- `DATA/.temp/` is the temporary staging area used by `scripts/download_dgb_data.sh`. +- `DATA/DGB_TG_network_datasets/raw_data/` is a mirror bundle of the same DGB datasets already unpacked into the individual dataset directories below; zero-only `.npy` files were pruned from both the top-level dataset folders and this mirror bundle, so it is not expanded again here. + +## Format Overview + +- Upstream raw DGB networks are originally stored as `.csv`, with one edge per line. +- The raw edge-list schema is `source_node,destination_node,timestamp,edge_label,edge_features...`. +- When a dataset has no edge label or no features, the baseline preprocessing may emit zero-filled placeholders for loading compatibility; this checkout later prunes all-zero `.npy` tensors instead of keeping them around. +- `ml_.csv`: baseline-friendly preprocessed edge list, typically `u,i,ts,label,idx`. +- `ml_.npy`: dense edge-feature matrix for the preprocessed edge list when it contains non-zero information. +- `ml__node.npy`: dense node-feature matrix when it contains non-zero information. +- `processed_data/edges.csv`: symlink to `raw_data/ml_.csv`. +- `processed_data/edge_feature.npy`: symlink to `raw_data/ml_.npy` when the edge-feature tensor exists and was not pruned as all zero, or a materialized downcast copy for selected integer-valued datasets. +- When both `.csv` and `ml_.csv` are present in the download, this checkout removes the redundant original `.csv` and keeps the preprocessed `ml_.csv`. +- In this checkout, the preprocessed `.npy` files often have one extra leading row for index alignment/padding, so their first dimension is usually `edge_count + 1` or `node_count + 1`. + +## `state_label` / `label` Notes + +- In DGB/DyGLib preprocessing, `ml_.csv.label` is copied from the raw `state_label` column when the raw CSV is available. +- `scripts/download_dgb_data.py` additionally extracts `processed_data/dynamic_node_labels.csv` from the second-to-last `label` column of `ml_mooc.csv`, `ml_reddit.csv`, and `ml_wikipedia.csv`. +- `MOOC`: `state(label)` means whether the student drops out after this action, i.e. whether this is the user's last action. In this checkout: `1 = 4,066`, `0 = 407,683`. +- `Wikipedia`: `state(label)` is the ban-state label, i.e. whether the user gets banned after this action. In this checkout: `1 = 217`, `0 = 157,257`. +- `Reddit`: `state(label)` is the user state-change label; on Reddit this specifically means whether the user gets banned after this interaction. In this checkout: `1 = 366`, `0 = 672,081`. +- `SocialEvo`: `state(label)` is degenerate in this checkout and is always `1` for every row. +- Other top-level datasets in this checkout have `state(label)` always equal to `0`. +- In the self-supervised link prediction pipelines used by DGB and DyGLib, these stored `state(label)` values are not used as link-prediction targets; positive and negative labels are created on the fly from observed edges and sampled negative edges. +- JODIE's original state-change setting does use these labels for user-state prediction tasks such as MOOC dropout prediction and Wikipedia/Reddit ban prediction. + +## Dataset Details + +- Source/destination ranges are computed from `DATA/DGB_*/raw_data/ml_*.csv` (`u`, `i`). +- `scripts/download_dgb_data.py` removes all-zero raw `*.npy` tensors, normalizes `CanParl`, `UNtrade`, and `UNvote` yearly timestamps to 0-based indices, downcasts `processed_data/edge_feature.npy` for `CanParl`, `Contacts`, `Flights`, `UNtrade`, `UNvote`, and `USLegis`, extracts `processed_data/dynamic_node_labels.csv` for `MOOC`, `Reddit`, and `Wikipedia`, and removes redundant non-`ml_*.csv` raw CSVs after the preprocessed `ml_*.csv` files are in place. +- DGB paper: [https://arxiv.org/pdf/2207.10128](https://arxiv.org/pdf/2207.10128) +- `Features & Labels` lists only non-`edges.csv` processed artifacts, shown as `size, rows×cols, dtype`. + +| Dataset | SRC_NID | DST_NID | Notes | Features & Labels | TS INFO | +| --- | --- | --- | --- | --- | --- | +| [CanParl](DGB_CanParl/processed_data/edges.csv) (num_edges: 74,478) | range: 1->734
unique: 734 | range: 2->734
unique: 244 | Canadian MP interaction network.
Edge weight = yearly count of shared "yes" votes on bills. | [edge_feature.npy](DGB_CanParl/processed_data/edge_feature.npy) 145.6KB, 74,479×1, int16 | yearly
range=[0->13]
unique=14 | +| [Contacts](DGB_Contacts/processed_data/edges.csv) (num_edges: 2,426,279) | range: 1->692
unique: 676 | range: 1->690
unique: 676 | University-student physical proximity network over one month.
Edge weight = proximity strength. | [edge_feature.npy](DGB_Contacts/processed_data/edge_feature.npy) 2.3MB, 2,426,280×1, int8 | n/a | +| [Flights](DGB_Flights/processed_data/edges.csv) (num_edges: 1,927,145) | range: 1->13169
unique: 11574 | range: 1->13169
unique: 12939 | Airport traffic during COVID-19.
Edge weight = number of flights between two airports in a day. | [edge_feature.npy](DGB_Flights/processed_data/edge_feature.npy) 3.7MB, 1,927,146×1, int16 | daily
range=[0->121]
unique=122 | +| [SocialEvo](DGB_SocialEvo/processed_data/edges.csv) (num_edges: 2,099,519) | range: 1->74
unique: 74 | range: 1->74
unique: 70 | Mobile phone proximity network in an undergraduate dorm over eight months.
Each edge has a 2-dim feature. | [edge_feature.npy](DGB_SocialEvo/processed_data/edge_feature.npy) 32.0MB, 2,099,520×2, float64 | Second
range=[0->20,935,623]
unique=565,932 | +| [UNtrade](DGB_UNtrade/processed_data/edges.csv) (num_edges: 507,497) | range: 1->255
unique: 255 | range: 1->255
unique: 254 | Food and agriculture trade between nations over 30+ years.
Edge weight = normalized import/export value. | [edge_feature.npy](DGB_UNtrade/processed_data/edge_feature.npy) 1.9MB, 507,498×1, int32 | yearly
range=[0->31]
unique=32 | +| [UNvote](DGB_UNvote/processed_data/edges.csv) (num_edges: 1,035,742) | range: 1->201
unique: 201 | range: 1->201
unique: 201 | UN General Assembly roll-call votes.
Edge weight increases when two nations both vote "yes". | [edge_feature.npy](DGB_UNvote/processed_data/edge_feature.npy) 2.0MB, 1,035,743×1, int16 | yearly
range=[0->71]
unique=72 | +| [USLegis](DGB_USLegis/processed_data/edges.csv) (num_edges: 60,396) | range: 1->225
unique: 224 | range: 1->225
unique: 225 | US Senate co-sponsorship network.
Edge weight = number of shared bill co-sponsorships in a congress. | [edge_feature.npy](DGB_USLegis/processed_data/edge_feature.npy) 118.1KB, 60,397×1, int16 | bi-yearly
range=[0->11]
unique=12 | +| [Enron](DGB_enron/processed_data/edges.csv) (num_edges: 125,235) | range: 1->184
unique: 181 | range: 1->184
unique: 184 | Email communications between Enron employees over three years. | n/a | Second
range=[0->113,740,399]
unique=22,632 | +| [LastFM](DGB_lastfm/processed_data/edges.csv) (num_edges: 1,293,103) | range: 1->980
unique: 980 | range: 981->1980
unique: 1000 | Bipartite user-song listening graph over one month. | [node_role.npy](DGB_lastfm/processed_data/node_role.npy) 2.1KB, 1,981×1, bool | Second
range=[0->137,107,267]
unique=1,283,614 | +| [MOOC](DGB_mooc/processed_data/edges.csv) (num_edges: 411,749) | range: 1->7047
unique: 7047 | range: 7048->7144
unique: 97 | Bipartite student-content interaction graph.
Each edge has a 4-dim feature. | [edge_feature.npy](DGB_mooc/processed_data/edge_feature.npy) 12.6MB, 411,750×4, float64
[dynamic_node_labels.csv](DGB_mooc/processed_data/dynamic_node_labels.csv) 2.0MB, 411,749×1, bool
[node_role.npy](DGB_mooc/processed_data/node_role.npy) 7.1KB, 7,145×1, bool | Second
range=[0->2,572,086]
unique=345,600 | +| [Reddit](DGB_reddit/processed_data/edges.csv) (num_edges: 672,447) | range: 1->10000
unique: 10000 | range: 10001->10984
unique: 984 | Bipartite user-subreddit posting graph over one month.
172-dim LIWC edge feature.
Dynamic ban labels. | [edge_feature.npy](DGB_reddit/processed_data/edge_feature.npy) 882.4MB, 672,448×172, float64
[dynamic_node_labels.csv](DGB_reddit/processed_data/dynamic_node_labels.csv) 3.3MB, 672,447×1, bool
[node_role.npy](DGB_reddit/processed_data/node_role.npy) 10.9KB, 10,985×1, bool | Second
range=[0->2,678,390.016]
unique=669,065 | +| [UCI](DGB_uci/processed_data/edges.csv) (num_edges: 59,835) | range: 1->1899
unique: 1350 | range: 1->1898
unique: 1862 | Online communication network where nodes are university students.
Edges are posted messages. | n/a | Second
range=[0->16,736,181]
unique=58,911 | +| [Wikipedia](DGB_wikipedia/processed_data/edges.csv) (num_edges: 157,474) | range: 1->8227
unique: 8227 | range: 8228->9227
unique: 1000 | Bipartite user-page editing graph over one month.
172-dim LIWC edge feature.
Dynamic temporary-ban labels. | [edge_feature.npy](DGB_wikipedia/processed_data/edge_feature.npy) 206.6MB, 157,475×172, float64
[dynamic_node_labels.csv](DGB_wikipedia/processed_data/dynamic_node_labels.csv) 772KB, 157,474×1, bool
[node_role.npy](DGB_wikipedia/processed_data/node_role.npy) 9.1KB, 9,228×1, bool | Second
range=[0->2,678,373]
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Dynamic Link Prediction + +## Dataset Description + +For preparing the datasets, we closely follow the baseline methods data preparation strategy. +The original networks are saved as .csv. + +The networks are formatted as follows: + * Each edge is denoted in one line. + * Each line has the following format: source_node, destination_node, timestamp, edge_label, comma-separated arrays of edge features. + * Please note that if there is no edge label available, the edge_label column will be filled with 0s only for loading purpose; these labels are not used in the link prediction task. + * The first line denotes the network format. + * Edge features should include at least one feature. If there is no edge feature available, a 0 value is used for all the edges. + +The network edge-lists are pre-processed for different methods to use them (Specifically, for preprocessing the data, we use the scripts available in "preprocess_data.py" file of the corresponding baseline). +Ater preprocessing the network edge-list, there are three files that are used by the models: + * .csv: this file contains the timestamp edge-list. + * .npy: this file contains the edge features in the dense `npy` format that has the features in binary format. + * .npy: this file contains the node features in the dense `npy` format that contains the node features in binary format. +Please note that when the edge features or node features are absent, we use a vector of zeros is used as the node/edge features in line with the baseline methods. \ No newline at end of file diff --git a/TG_network_datasets/raw_data/enron/ml_enron.csv b/TG_network_datasets/raw_data/enron/ml_enron.csv new file mode 100644 index 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